Integrating Health Informatics into Pre-Registration Nursing Education: Insights from a Participatory Workshop
Bibliographic record
Abstract
The implementation of health informatics in pre-registration health professional degrees faces persistent challenges, including curriculum overload, educator workforce capability gaps, and financial constraints. Despite these barriers, reports of successful implementation of health informatics pre-registration nursing programs exist. A virtual workshop was held during thein 15th International Nursing Informatics Conference in 2021 with the aim to explore successful implementation strategies for incorporating health informatics into the nursing curriculum to meet the accreditation standards. This paper reports recommendations from the workshop emphasising the importance academic-clinical partnerships to develop innovative approaches to enhance theof capacity of academic teams and access to contemporary point of care digital technologies that reflect applications of health informatics in interdisciplinary clinical settings.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.073 | 0.061 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.022 | 0.013 |
| Scholarly communication | 0.012 | 0.006 |
| Open science | 0.004 | 0.019 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".